Electronic computing devices are becoming increasingly ubiquitous in the modern world. Whether utilized for business, entertainment, communication, security or numerous other purposes, the capabilities of such devices continue to expand. Along with the improvements made in terms of processing power, rendering technology, memory, power consumption and other aspects, various applications have also been developed to utilize the expanded capabilities of computing devices. However, the expansion of capabilities with respect to such devices has also introduced new sets of challenges as further improvements are sought and new applications are developed.
One area in which the use of electronic computing devices has presented new challenges relates to computer vision. Computer vision utilizes machines to see. As such, for example, computer vision often employs cameras and other elements to build systems that can obtain information from image data such as a video sequence, views from multiple cameras or multidimensional data from scanning devices. Computer vision may be useful for many tasks such as: controlling processes or device movements; detecting and/or recognizing events, objects, patterns or people; organizing information; and/or the like. Accordingly, computer vision may be considered to be an artificial vision system, which may be implemented in combinations of various devices and applications.
The tracking of objects and/or regions of interest within a series of video frames has been a longstanding problem in computer vision scenarios. In particular, it has been difficult to provide robust tracking capabilities for objects or regions of interest that may undergo significant changes (e.g., illumination changes, pose or aspect changes, occlusions, and/or the like). Image-patch based and feature-based methods of object tracking have been proposed in the past. Image-patch based tracking has been considered by some to be suitable for tracking rigid and non-rigid objects that may undergo significant pose changes. However, traditional image-patch based methods have tended to suffer from a drifting template problem (e.g., accumulated error in template updating leading to a tracking failure) and may be sensitive to partial occlusion. Feature-based methods have been considered by some to be insensitive to partial occlusion, but have traditionally proven to be less useful for tracking objects that undergo large pose changes. Accordingly, improvements in the area of object tracking may be desirable.